Wireless signal digital conversion device

ABSTRACT

A wireless signal digital conversion device according to an embodiment of the present disclosure comprises: a plurality of RF signal processing units for receiving wireless signals, and adjusting the voltage levels of the wireless signals on the basis of automatic gain values; a plurality of unit ADCs for receiving in-phase signals and quadrature phase signals of the RF signal processing units, and performing analog-to-digital conversion on the basis of different differential reference voltages; an encoder unit for generating binary data having less bits than binary data outputted from the unit ADCs on the basis of the binary data outputted from the unit ADCs; and an automatic gain control unit for generating automatic gain values on the basis of the output of a spatial ADC. A terminal of the present disclosure can be linked to an artificial intelligence module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 6G services, and the like.

TECHNICAL FIELD

The present disclosure relates to a digital conversion device of awireless signal and, more particularly, to provide a digital conversiondevice of a wireless signal which can lower power consumption andincrease a reception power of a wireless signal.

BACKGROUND ART

A wireless communication system is widely deployed to provide varioustypes of communication services such as voice and data. In general, awireless communication system is a multiple access system capable ofsupporting communication with multiple users by sharing available systemresources (bandwidth, transmission power, etc.). Examples of themultiple access system include a Code Division Multiple Access (CDMA)system, a Frequency Division Multiple Access (FDMA) system, a TimeDivision Multiple Access (TDMA) system, a Space Division Multiple Access(SDMA) system, and an Orthogonal Frequency Division Multiple Access(OFDMA) syste, SC-FDMA (Single Carrier Frequency Division MultipleAccess) system, IDMA (Interleave Division Multiple Access) system, andthe like.

As the wireless environment requires a wider bandwidth in the highfrequency band, the path attenuation becomes serious, and thesignal-to-noise ratio (SNR) is relatively lowed.

A multiple antenna system is used to obtain a high antenna gain, but theconventional multiple antenna system requires a high-resolution analogto digital converter (hereinafter, ADC) with heavy power consumption.

DISCLOSURE Technical Problem

The present disclosure is to solve the requirement and/or problem.

In addition, the present disclosure is to implement an analogue todigital conversion device of a wireless signal which can lower powerconsumption and increase resolution.

Technical Solution

According to an embodiment of the present disclousure, a digitalconversion device of a wireless signal, comprising: a plurality ofantennas for receiving a reception signal of a predetermined range ofreception voltage level; a plurality of unit ADC portions one-to-onematched to the antennas and for converting a partial voltage levelwithin the range of the reception voltage level of the reception signalto binary data, wherein the plurality of unit ADC portions converts eachof the partial voltage level having different voltage level to binarydata; and an encoder for generating a digital code that a voltage levelcorresponding to the reception voltage level is converted to binary databased on the binary data output from the unit ADC portions.

Furthermore, wherein each of the unit ADC portions includes: a pluralityof RF signal processors for adjusting the voltage level of the receptionsignal based on an automatic gain value; and a plurality of unit ADCsfor receiving an input of an in-phase signal and a quadrature-phasesignal of each of the RF signal processors, wherein each of theplurality of unit ADCs performs an analogue to digital conversion basedon a differential reference voltage.

Furthermore, wherein the encoder generates binary data of a bit lowerthan a bit of the binary data based on the binary data output from theunit ADCs.

Furthermore, further comprising an automatic gain controller forgenerating the automatic gain value based on an output of the spatialADC.

Furthermore, wherein the encoder includes: a plurality of unit encodersfor generating a thermometer code from respective binary data outputfrom the unit ADCs; and a spatial encoder for generating binary data ofa smaller bit based on the binary data output from each of the unitencoders.

Furthermore, wherein each of the unit encoders outputs data of 1-bit.

Furthermore, wherein the encoder includes m unit encoders, and whereinthe encoder generates binary data of n-bit (n is a natural numbersmaller than m) based on m binary data (m is a natural number) outputfrom the unit encoders, and the binary data of n-bit is generated to beproportional to a value of “1” among the m binary data.

Furthermore, wherein the number of unit encoders is set to “2n−1”.

Furthermore, wherein the differential reference voltage input to each ofthe m spatial encoders is proportional by an integer multiple with eachother.

Furthermore, wherein each of the RF signal processors is provided withthe automatic gain value of a same size.

Furthermore, wherein the automatic gain value includes: a firstautomatic gain value input to a low noise amplifier and a secondautomatic gain value input to a variable gain adjuster.

Furthermore, wherein the first automatic gain value is adjusted suchthat a different of output signal levels among the low noise amplifiersis less than a preconfigured threshold value.

Furthermore, wherein the automatic gain controller controls the firstand second automatic gain values to deactivate the RF signal processorto which the reception antenna is belonged, based on the voltage levelof a signal received through an arbitrary antenna being a preconfiguredthreshold voltage or lower.

Furthermore, wherein the automatic gain controller generates theautomatic gain value based on outputs of the unit encoders or thespatial encoders.

Furthermore, wherein the automatic gain controller calculates aninstantaneous power based on a square of the in-phase signal and asquare of the quadrature-phase signal and calculates the automatic gainvalue based on a hysteresis of the difference value between theinstantaneous power and a target power value.

Furthermore, wherein each of the spatial ADCs operates by using a commonclock.

Furthermore, wherein each of the spatial ADCs digital-converts thepartial voltage level on a different timing.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the present disclosure and constitute a part of thedetailed description, illustrate embodiments of the present disclosureand together with the description serve to explain the principle of thepresent disclosure.

FIG. 1 illustrates physical channels and general signal transmissionused in a 3GPP system.

FIG. 2 is a diagram illustrating an example of a communication structureprovidable in a 6G system.

FIG. 3 illustrates a perceptron structure.

FIG. 4 illustrates a multi-perceptron structure.

FIG. 5 illustrates a deep neural network structure.

FIG. 6 illustrates a convolutional neural network structure.

FIG. 7 illustrates a filter operation in a convolutional neural network.

FIG. 8 illustrates a neural network structure in which a recurrent loopis present.

FIG. 9 illustrates an operating structure of a recurrent neural network.

FIG. 10 illustrates an example of an electromagnetic spectrum.

FIG. 11 illustrates an example of THz communication application.

FIG. 12 is a diagram illustrating a digital conversion device of awireless signal according to an embodiment of the present disclosure.

FIG. 13 is a diagram illustrating an operation of the automatic gaincontroller according to the first embodiment.

FIG. 14 is a diagram illustrating an operation of the automatic gaincontroller according to the first embodiment.

FIG. 15 is a diagram illustrating a digital conversion device of awireless signal according to another embodiment of the presentdisclosure.

FIG. 16 is a diagram illustrating a digital conversion device of awireless signal according to still another embodiment of the presentdisclosure.

FIG. 17 illustrates a communication system applied to the presentdisclosure.

FIG. 18 illustrates a wireless device that can be applied to the presentdisclosure.

FIG. 19 illustrates a signal processing circuit for a transmissionsignal.

FIG. 20 shows another example of a wireless device applied to thepresent disclosure.

FIG. 21 illustrates a portable device applied to the present disclosure.

FIG. 22 illustrates a vehicle or an autonomous vehicle to which thepresent disclosure is applied.

FIG. 23 illustrates a vehicle applied to the present disclosure.

FIG. 24 illustrates an XR device applied to the present disclosure.

FIG. 25 illustrates a robot applied to the present disclosure.

FIG. 26 illustrates an AI device applied to the present disclosure.

MODE FOR DISCLOSURE

Hereinafter, an embodiment disclosed in the present disclosure will bedescribed in detail with reference to the accompanying drawings and thesame or similar components are denoted by the same reference numeralsregardless of a sign of the drawing, and duplicated description thereofwill be omitted. Suffixes “module” and “unit” for components used in thefollowing description are given or mixed in consideration of easypreparation of the present disclosure only and do not have their owndistinguished meanings or roles. Further, in describing an embodimentdisclosed in the present disclosure, a detailed description of relatedknown technologies will be omitted if it is determined that the detaileddescription makes the gist of the embodiment of the present disclosureunclear. Further, it is to be understood that the accompanying drawingsare just used for easily understanding the embodiments disclosed in thepresent disclosure and a technical spirit disclosed in the presentdisclosure is not limited by the accompanying drawings and all changes,equivalents, or substitutes included in the spirit and the technicalscope of the present disclosure are included.

Terms including an ordinary number, such as first and second, are usedfor describing various elements, but the elements are not limited by theterms. The terms are used only to discriminate one element from anotherelement.

It should be understood that, when it is described that a component is“connected to” or “accesses” another component, the component may bedirectly connected to or access the other component or a third componentmay be present therebetween. In contrast, when it is described that acomponent is “directly connected to” or “directly accesses” anothercomponent, it is understood that no element is present between theelement and another element.

A singular form includes a plural form if there is no clearly oppositemeaning in the context.

In the present application, it should be understood that term “include”or “have” indicates that a feature, a number, a step, an operation, acomponent, a part or the combination thereof described in the presentdisclosure is present, but does not exclude a possibility of presence oraddition of one or more other features, numbers, steps, operations,components, parts or combinations thereof, in advance.

The following technology may be used in various radio access systemincluding CDMA, FDMA, TDMA, OFDMA, SC-FDMA, and the like. The CDMA maybe implemented as radio technology such as Universal Terrestrial RadioAccess (UTRA) or CDMA2000. The TDMA may be implemented as radiotechnology such as a global system for mobile communications(GSM)/general packet radio service (GPRS)/enhanced data rates for GSMevolution (EDGE). The OFDMA may be implemented as radio technology suchas Institute of Electrical and Electronics Engineers (IEEE) 802.11(Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, Evolved UTRA (E-UTRA), or thelike. The UTRA is a part of Universal Mobile Telecommunications System(UMTS). 3rd Generation Partnership Project (3GPP) Long Term Evolution(LTE) is a part of Evolved UMTS (E-UMTS) using the E-UTRA andLTE-Advanced (A)/LTE-A pro is an evolved version of the 3GPP LTE. 3GPPNR (New Radio or New Radio Access Technology) is an evolved version ofthe 3GPP LTE/LTE-A/LTE-A pro. 3GPP 6G may be an evolved version of 3GPPNR.

For clarity of description, the technical spirit of the presentdisclosure is described based on the 3GPP communication system (e.g.,LTE or NR etc), but the technical spirit of the present disclosure arenot limited thereto. LTE means technology after 3GPP TS 36.xxx Release8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 is referredto as the LTE-A and LTE technology after 3GPP TS 36.xxx Release 13 isreferred to as the LTE-A pro. The 3GPP NR means technology after TS38.xxx Release 15. 3GPP 6G may mean technology after TS Release 17and/or Release 18. “xxx” means standard document detail number.LTE/NR/6G may be collectively referred to as a 3GPP system. Forbackground art, terms, abbreviations, etc. used in the description ofthe present disclosure, reference may be made to matters described instandard documents published prior to the present disclosure. Forexample, the following document can be referred to.

3GPP LTE

-   -   36.211: Physical channels and modulation    -   36.212: Multiplexing and channel coding    -   36.213: Physical layer procedures    -   36.300: Overall description    -   36.331: Radio Resource Control (RRC)

3GPP NR

-   -   38.211: Physical channels and modulation    -   38.212: Multiplexing and channel coding    -   38.213: Physical layer procedures for control    -   38.214: Physical layer procedures for data    -   38.300: NR and NG-RAN Overall Description    -   38.331: Radio Resource Control (RRC) protocol specification

Physical Channels and Frame Structure

Physical Channel and General Signal Transmission

FIG. 1 illustrates physical channels and general signal transmissionused in a 3GPP system. In a wireless communication system, the UEreceives information from the eNB through Downlink (DL) and the UEtransmits information from the eNB through Uplink (UL). The informationwhich the eNB and the UE transmit and receive includes data and variouscontrol information and there are various physical channels according toa type/use of the information which the eNB and the UE transmit andreceive.

When the UE is powered on or newly enters a cell, the UE performs aninitial cell search operation such as synchronizing with the eNB (S11).To this end, the UE may receive a Primary Synchronization Signal (PSS)and a (Secondary Synchronization Signal (SSS) from the eNB andsynchronize with the eNB and acquire information such as a cell ID orthe like. Thereafter, the UE may receive a Physical Broadcast Channel(PBCH) from the eNB and acquire in-cell broadcast information.Meanwhile, the UE receives a Downlink Reference Signal (DL RS) in aninitial cell search step to check a downlink channel status.

A UE that completes the initial cell search receives a Physical DownlinkControl Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH)according to information loaded on the PDCCH to acquire more specificsystem information (S12).

Meanwhile, when there is no radio resource first accessing the eNB orfor signal transmission, the UE may perform a Random Access Procedure(RACH) to the eNB (S13 to S16). To this end, the UE may transmit aspecific sequence to a preamble through a Physical Random Access Channel(PRACH) (S13 and S15) and receive a response message (Random AccessResponse (RAR) message) for the preamble through the PDCCH and acorresponding PDSCH. In the case of a contention based RACH, aContention Resolution Procedure may be additionally performed (S16).

The UE that performs the above procedure may then perform PDCCH/PDSCHreception (S17) and Physical Uplink Shared Channel (PUSCH)/PhysicalUplink Control Channel (PUCCH) transmission (S18) as a generaluplink/downlink signal transmission procedure. In particular, the UE mayreceive Downlink Control Information (DCI) through the PDCCH. Here, theDCI may include control information such as resource allocationinformation for the UE and formats may be differently applied accordingto a use purpose.

Meanwhile, the control information which the UE transmits to the eNBthrough the uplink or the UE receives from the eNB may include adownlink/uplink ACK/NACK signal, a Channel Quality Indicator (CQI), aPrecoding Matrix Index (PMI), a Rank Indicator (RI), and the like. TheUE may transmit the control information such as the CQI/PMI/RI, etc.,through the PUSCH and/or PUCCH.

Structures of Uplink and Downlink Channels

Downlink Channel Structure

The BS transmits an associated signal to the UE through a downlinkchannel to be described below and the UE receives the associated signalfrom the BS through the downlink channel to be described below.

(1) Physical Downlink Shared Channel (PDSCH)

The PDSCH transports downlink data (e.g., DL-shared channel transportblock (DL-SCH TB)), and adopts modulation methods such as QuadraturePhase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64QAM, and 256 QAM. A codeword is generated by encoding a TB. The PDSCHmay carry multiple codewords. Scrambling and modulation mapping areperformed for each codeword and modulation symbols generated from eachcodeword are mapped to one or more layers (layer mapping). Each layer ismapped to a resource together with a demodulation reference signal(DMRS), generated as an OFDM symbol signal, and transmitted through acorresponding antenna port.

(2) Physical Downlink Control Channel (PDCCH)

The PDCCH transports downlink control information (DCI) and a QPSKmodulation method is applied. One PDCCH is constituted by 1, 2, 4, 8,and 16 Control Channel Elements (CCEs) according to an Aggregation Level(AL). One CCE is constituted by 6 Resource Element Groups (REGs). OneREG is defined by one OFDM symbol and one (P)RB.

The UE performs decoding (so-called, blind decoding) for a set of PDCCHcandidates to obtain the DCI transmitted through the PDCCH. The set ofPDCCH candidates decoded by the UE is defined as a PDCCH search spaceset. The search space set may be a common search space or a UE-specificsearch space. The UE may obtain the DCI by monitoring PDCCH candidatesin one or more search space sets configured by the MIB or higher layersignaling.

Uplink Channel Structure

The UE transmits an associated signal to the BS through an uplinkchannel to be described below and the BS receives the associated signalfrom the UE through the uplink channel to be described below.

(1) Physical Uplink Shared Channel (PUSCH)

The PUSCH transports uplink data (e.g., UL-shared channel transportblock (UL-SCH TB) and/or uplink control information (UCI) and istransmitted based on a Cyclic Prefix—Orthogonal Frequency DivisionMultiplexing (CP-OFDM) waveform or a Discrete FourierTransform—spread—Orthogonal Frequency Division Multiplexing (DFT-s-OFDM)waveform. When the PUSCH is transmitted based on the DFT-s-OFDMwaveform, the UE transmits the PUSCH by applying transform precoding. Asan example, when the transform precoding is disable (e.g., transformprecoding is disabled), the UE transmits the PUSCH based on the CP-OFDMwaveform, and when the transform precoding is enabled (e.g., transformprecoding is enabled), the UE may transmit the PUSCH based on theCP-OFDM waveform or the DFT-s-OFDM waveform. PUSCH transmission isdynamically scheduled by the UL grant in the DCI or semi-staticallyscheduled based on higher layer (e.g., RRC) signaling (and/or Layer 1(L1) signaling (e.g., PDCCH)) (configured grant). The PUSCH transmissionmay be performed based on a codebook or a non-codebook.

(2) Physical Uplink Control Channel (PUCCH)

The PUCCH may transport uplink control information, HARQ-ACK, and/orscheduling request (SR), and may be divided into multiple PUCCHsaccording to a PUCCH transmission length.

General Contents of 6G System

A 6G (wireless communication) system has purposes including (i) a veryhigh data rate per device, (ii) a very large number of connecteddevices, (iii) global connectivity, (iv) a very low latency, (v)reduction of energy consumption of battery-free IoT devices, (vi)ultrahigh reliability connection, (vii) connected intelligence having amachine learning capability, etc. A vision of the 6G system may be fouraspects, .e.g., intelligent connectivity, deep connectivity, holographicconnectivity, and ubiquitous connectivity, and the 6G system may satisfyrequirements shown in Table 1 below. That is, Table 1 shows an exampleof the requirements of the 6G system.

TABLE 1 Per device peak data rate 1 Tbps E2E latency 1 ms Maximumspectral efficiency 100 bps/Hz Mobility support Up to 1000 km/hrSatellite integration Fully AI Fully Autonomous vehicle Fully XR FullyHaptic Communication Fully

The 6G system may have key factors such as Enhanced mobile broadband(eMBB), Ultra-reliable low latency communications (URLLC), massivemachine-type communication (mMTC), AI integrated communication, Tactileinternet, High throughput, High network capacity, High energyefficiency, Low backhaul and access network congestion, and Enhanceddata security. FIG. 2 is a diagram illustrating an example of acommunication structure providable in a 6G system.

It is anticipated that the 6G system has higher simultaneous wirelesscommunication connectivity which is 50 times higher than the 5G wirelesscommunication system. The URLLC which is the key feature of 5G will be amore important technology by providing a smaller end-to-end latency than1 ms in the 6G communication. In the 6G system, volume spectrumefficiency will be even more excellent unlike region spectrum efficiencyfrequently used. The 6G system may provide an advanced batterytechnology for a very long battery life-span and energy harvesting. In6G, new network features may be as follows.

-   -   Satellites integrated network: It is anticipated that the 6G        will be integrated with the satellite in order to provide a        global mobile group. Integrating the terrestrial, the satellite,        and the aerial network into one wireless communication system is        very important for the 6G.    -   Connected intelligence: Unlike the wireless communication system        of the previous generation, the 6G is innovative, and wireless        evolution will be updated from “connected things” to “connected        intelligence”. The AI may be applied in each step (or each        procedure of signal processing to be described below) of a        communication procedure.    -   Seamless integration wireless information and energy transfer.        The 6G wireless network will transfer power in order to charge        batteries of devices like smartphones and sensors. Therefore,        wireless information and energy transmission (WIET) will be        integrated.    -   Ubiquitous super 3D connectivity: Connection to networks of        networks and core network functions of a drone and a very low        earth orbit network will make a super 3D connection in 6G        ubiquitous.

Several general requirements may be as follows in new network featuresof the 6G described above.

-   -   Small cell networks: An ID of the small cell network is        introduced in order to enhance a received signal quality as a        result of enhancement of a throughput, energy efficiency and        spectrum efficiency in a cellular system. Consequently, the        small cell network is a required feature of communication        systems of 6G and beyond 5G (5 GB) or more. Therefore, the 6G        communication system also adopts the feature of the small cell        network.    -   Ultra-dense heterogeneous network: Ultra-dense heterogeneous        networks will become another important feature of the 6G        communication system. A multi-tier network constituted by        heterogeneous networks improves entire QoS and reduces cost.    -   High-capacity backhaul: The backhaul connectivity is        characterized as the high-capacity backhaul network in order to        support high-capacity traffic. A high-speed optical fiber and        free space optic system may be an available solution for such a        problem.    -   Radar technology integrated with mobile technology:        High-precision localization (location based service) through        communication is one of the functions of the 6G wireless        communication system. Therefore, a radar system will be        integrated with the 6G network.    -   Softwarization and virtualization: The softwarization and        virtualization are two important functions which become a basis        of a design process in the 5 GB network in order to guarantee        reconfigurability and programmability. Further, in a sharing        physical infrastructure, billions of devices may be shared.

Core Implementation Technology of 6G System

Artificial Intelligence

The AI is most important in the 6G system, and is a technology to benewly introduced. The AI is not involved in the 4G system. The 5G systemwill support a partial or very limited AI. However, the 6G system willcompletely support the AI for automation. Development of machinelearning will make a more intelligent network for real-timecommunication in the 6G. When the AI is introduced into communication,real-time data transmission may be simplified and enhanced. The AI maydetermine a scheme in which a complicated target task is performed byusing numerous analyses. That is, the AI may increase efficiency anddecrease the processing latency.

Time-consuming task such as handover, network selection, and resourcescheduling may be immediately performed by using the AI. The AI may playan important role even in M2M, machine-to-human, and human-to-machinecommunication. Further, the AI may become rapid communication in a braincomputer interface (BCI). An AI based communication system may besupported by a metal material, an intelligent structure, an intelligentnetwork, an intelligent device, an intelligent cognition radio, aself-maintenance network, and machine learning.

In recent years, an attempt to integrate the AI with the wirelesscommunication system has appeared, but this concentrates an applicationlayer and a network layer, in particular, deep learning on a wirelessresource management and allocation field. However, this study isgradually developed to a MAC layer and a physical layer, and inparticular, there are attempts to combine the deep learning withwireless transmission in a physical layer. AI based physical layertransmission means not a traditional communication framework, but asignal processing and communication mechanism based on an AI driver in afundamental signal processing and communication mechanism. For example,the AI based physical layer transmission may include channel coding anddecoding, deep learning based signal estimation and detection, a deeplearning based MIMI mechanism, AI based resource scheduling andallocation, etc.

The machine learning may be used for channel estimation and channeltracking, and used for power allocation, interference cancellation,etc., in a downlink physical layer. Further, the machine learning mayalso be used for antenna selection, power control, symbol detection,etc., in an MIMO system.

However, application of DNN for transmission in the physical layer mayhave the following problem.

In a deep learning based AI algorithm, numerous training data arerequired to optimize training parameters. However, due to a limit inacquiring data in a specific channel environment as the training data, alot of training data are used offline. This, in the specific channelenvironment, with respect to static training for the training data, acontradiction may occur between dynamic features and diversity of aradio channel.

Further, current deep learning primarily targets an actual real signal.However, signals of the physical layer of the wireless communication arecomplex signals. In order to match features of wireless communicationsignals, a study for a neural network which detects a complex domainsignal is further required.

Hereinafter, the machine learning will be described in more detail.

The machine learning means a series of operations of training themachine in order to make a machine which may perform a task which aperson may perform or which it is difficult for the person to perform.Data and a learning model are required for the machine learning. Alearning method of the data in the machine learning may be generallyclassified into three, i.e., supervised learning, unsupervised learning,and reinforcement learning.

Learning of a neural network is to minimize an error of an output. Thelearning of the neural network is a process of repeatedly inputtinglearning data into the neural network and calculating the output of theneural network for the learning data and the error of a target andback-propagating the errors of the neural network from the output layerof the neural network toward the input layer in a direction to reducethe errors to update the weight of each node of the neural network.

The supervised learning may use learning data in which the learning datais labeled with a correct answer, and the unsupervised learning may uselearning data in which not be labeled with the correct answer. That isfor example, the learning data in the case of the supervised learningrelated to the data classification may be data in which category islabeled in each learning data. The labeled learning data is input to theneural network, and the error may be calculated by comparing the output(category) of the neural network with the label of the learning data.The calculated error is back-propagated in a reverse direction (i.e., adirection from the output layer toward the input layer) in the neuralnetwork and connection weights of respective nodes of each layer of theneural network may be updated according to the back propagation. Avariation amount of the updated connection weight of each node may bedetermined according to a learning rate. Calculation of the neuralnetwork for the input data and the back-propagation of the error mayconstitute a learning cycle (epoch). A learning rate may be applieddifferently according to the number of repetition times of the learningcycle of the neural network. For example, in an initial stage of thelearning of the neural network, the neural network ensures a certainlevel of performance quickly by using a high learning rate, therebyincreasing efficiency and a low learning rate is used in a latter stageof the learning, thereby increasing accuracy.

The learning method may vary depending on a feature of data. Forexample, when a purpose is to accurately predict data by a transmitterby a receiver on the communication system, it is more preferable toperform learning by using the supervised learning than the unsupervisedlearning or reinforcement learning.

The learning model corresponds to a human brain, and a most basic linearmodel may be considered, but a paradigm of a machine learning a neuralnetwork structure having high complexity such as artificial neuralnetworks as the learning model is referred to the deep learning.

A neural network core used as the learning scheme generally includesdeep neural networks (DNN), convolutional deep neural networks (CNN),and recurrent neural networks (RNN) (recurrent Boltzman machine)schemes.

The artificial neural network is an example of connecting severalperceptrons.

Referring to FIG. 3 , when an input vector x=(18, 19, . . . , xd) isinput, an entire process of multiplying each component by weights (17,W2, . . . , Wd), and aggregating all of the results are aggregated, andthen applying an active function σ(·) is referred to as perceptron. Amassive artificial neural network structure extends a simplifiedperceptron structure illustrated in FIG. 3 to apply the input vector todifferent multi-dimension perceptrons. For convenience of description,an input value or an output value is referred to as a node.

Meanwhile, it may be described that the perceptron structure illustratedin FIG. 3 is constituted by a total of three layers based on the inputvalue and the output value. An artificial neural network in which thereare H (d+1)-dimension perceptrons between a 1st layer and a 2nd layerand K (H+1)-dimension perceptrons between the 2nd layer and a 3rd layermay be expressed as in FIG. 4 .

An input layer on which the input vector is located is referred to as aninput layer, an output layer on which a final output value is located isreferred to as an output layer, and all layers located between the inputlayer and the output layer are referred to as a hidden layer. In theillustration of FIG. 4 , three layers are disclosed, but when the actualnumber of artificial neural network layers is counted, the number iscounted except for the input layer, so the actual number of artificialneural network layers may be regarded as a total of two layers. Theartificial neural network is configured by connecting a perceptron of abasic block in two dimensions.

The input layer, hidden layer, and output layer may be commonly appliedin various artificial neural network structures such as CNN, RNN, etc.,to be described below in addition to a multi-layer perceptron. As thenumber of hidden layers increases, the artificial neural network isdeeper, and a machine learning paradigm that uses a sufficientlydeepened artificial neural network as the learning model is referred toas deep learning. Further, the artificial neural network used for thedeep learning is referred to as a deep neural network (DNN).

The DNN illustrated in FIG. 5 is a multi-layer perceptron constituted byeight hidden layer+output layer. The multi-layer perceptron structure isexpressed as a fully-connected neural network. In the fully-connectedneural network, there is no connection relationship between nodeslocated on the same layer, and there is the connection relationship onlybetween nodes located on adjacent layers. The DNN has a fully-connectedneural network structure, and is configured by a combination of multiplehidden layers and active functions, and may be usefully applied todetermining correlation features between an input and an output. Her,the correlation features may mean a joint probability of the input andthe output.

Meanwhile, various artificial neural network structures different fromthe DNN may be formed according to how a plurality of perceptrons beingconnected to each other.

In the DNN, nodes located inside one layer are disposed in a1-dimensional vertical direction. However, in FIG. 6 , a case where whorizontal nodes and h vertical nodes are disposed in two dimensionswith respect to the nodes may be assumed (a convolutional neural networkstructure of FIG. 6 ). In this case, since a weight is added per oneconnection in a connection process connected from one input node to thehidden layer, a total of h×w weights should be considered. Since thereare h×w nodes in the input layer, a total of h2w2 weights are requiredbetween two adjacent layers.

The convolutional neural network of FIG. 6 has a problem in that thenumber of weights exponentially increases according to the number ofconnections, so by assuming that there is a small-sized filter insteadof considering all mode connections between the adjacent layers, aweighted sum and an active function operation are performed with respectto a portion where the filters are overlapped as illustrated I FIG. 7 .

One filter has weights corresponding to a number as large as a sizethereof, and the weight may be learned so as to extract and output anyspecific feature on the image as a factor. In FIG. 7 , a 3×3-sizedfilter is applied to an uppermost left end 3×3 region of the inputlayer, and an output value of a result of performing the weighted sumand the active function operation for the corresponding node is storedin z22.

The filter performs the weighted sum and active function operation whilemoving at predetermined horizontal and vertical intervals while scanningthe input layer, and the output value thereof is located at a positionof a current filter. ‘Such an operation scheme is similar to aconventional operation for an image in a computer vision field, so theDNN having such a structure is referred to as a convolutional neuralnetwork (CNN) and a hidden layer generated as the result of theconvolutional operation is referred to as a convolutional layer.Further, a neural network in which there is a plurality of convolutionallayers is referred to as a deep convolutional neural network (DCNN).

In the convolutional layer, in the node at which the current filter islocated, only a node located at a region covered by the filter isincluded to calculate the weighted sum, thereby reducing the number ofweights. As a result, one filter may be used to concentrate on a featurefor a local region. As a result, the CNN may be effectively applied toimage data processing in which a physical distance in a 2D regionbecomes an important determination criteron. Meanwhile, in the CNN, aplurality of filters may be applied just before the convolutional layer,and a plurality of output results may also be generated through theconvolutional operation of each filter.

Meanwhile, there may be data in which sequence features are important. Astructure applying a scheme of inputting one element on a data sequenceevery timestep by considering a length variability and a time order ofthe sequence data, and inputting an output vector (hidden vector) of thehidden layer output at a specific time jointly with a just next elementon the sequence to the artificial neural network is referred to arecurrent neural network structure.

Referring to FIG. 8 , the recurrent neural network (RNN) is a structureof applying the weighted sum and the active function by jointlyinputting hidden vectors (z1(t−1), z2(t−1), . . . , zH(t−1)) at animmediately previous timet−1 in the process of elements (18(t), 19(t), .. . , xd(t)) at any time t on the data sequence. A reason fortransferring the hidden vector to a subsequent time is that it isregarded that information in the input vector at the previous times isaccumulated in the hidden vector at the current time.

Referring to FIG. 8 , the RNN operates according to a predetermined timeorder with respect to the input data sequence.

A hidden vector (z1(1), z2(1), . . . , zH(1)) when an input vector(18(t), 19(t), . . . , xd(t)) at time 1 is input into the RNN is inputjointly with an input vector (18(2), 19(2), . . . , xd(2)) at time 2 todetermine a vector (z1(2), z2(2), . . . , zH(2)) of the hidden layerthrough the weighted sum and the active function. Such a process isrepeatedly performed up to time 2, time 3, . . . , time T.

Meanwhile, when a plurality of hidden layers is disposed in the RNN,this is referred to as a deep recurrent neural network (DRNN). The RNNis usefully applied to the sequence data (e.g., natural languageprocessing) and designed.

The neural network core used as the learning scheme may include variousdeep learning techniques such as a Restricted Boltzman Machine (RBM),deep belief networks (DBN), and a Deep Q-Network in addition to the DNN,the CNN, and the RNN, and may be applied to fields such as computervision, voice recognition, natural language processing, voice/signalprocessing, etc.

In recent years, an attempt to integrate the AI with the wirelesscommunication system has appeared, but this concentrates an applicationlayer and a network layer, in particular, deep learning on a wirelessresource management and allocation field. However, this study isgradually developed to a MAC layer and a physical layer, and inparticular, there are attempts to combine the deep learning withwireless transmission in a physical layer. AI based physical layertransmission means not a traditional communication framework, but asignal processing and communication mechanism based on an AI driver in afundamental signal processing and communication mechanism. For example,the AI based physical layer transmission may include channel coding anddecoding, deep learning based signal estimation and detection, a deeplearning based MIMI mechanism, AI based resource scheduling andallocation, etc.

Terahertz (THz) Communication

The data rate may increase a bandwidth. This may be performed by usingsub-THz communication with a wide bandwidth, and applying an advancedmassive MIMO technology. A THz wave also known as a radiation ofmillimeters or less generally shows a frequency band between 0.1 THz and10 THz having a corresponding wavelength in the range of 0.03 mm to 3mm. A 100 GHz to 300 GHz band range (sub THz band) is regarded as aprimary part of the THz for cellular communication. When the sub-THzband is added to a mmWave band, a 6G cellular communication capacityincreases. 300 GHz to 3 THz in a defined THz band is present in aninfrared (IR) frequency band. The 300G Hz to 3THz band is a part of awideband, but is present on a boundary of the wideband, and is presentjust behind an RF band. Therefore, the 300G Hz to 3THz band shows asimilarity to the RF. FIG. 10 illustrates an example of anelectromagnetic spectrum.

The THz wave may be located between the radio frequency (RF)/millimeter(mm), and the IR band, and (i) may better transmit anon-metallic/non-polarizable material than visible light/infrared rays,and have a smaller wavelength than the RF/millimeter wave, and has ahigh straightness, and may be capable of focusing a beam. Further, sincephoton energy of the THz wave is only a few meV, the photon energy isharmless to the human body. A frequency band which is expected to beused for the THz wireless communication may be a D-band (110 GHz to 170GHz) or an H-band (220 GHz to 325 GHz) having small radio wave loss bymolecular absorption in the air. The standardization of the THz wirelesscommunication is discussed around the IEEE 802.15 THz working group inaddition to 3GPP, and a standard document issued by the IEEE 802.15 TaskGroup (TG3d, TG3e) may embody or supplement the contents described inthe present disclosure. The THz wireless communication may be applied inwireless cognition, sensing, imaging, wireless communication, THznavigation, etc. Primary features of the THz communication include (i) abandwidth widely usable to support a very high data rate, and (ii) ahigh path loss generated at a high frequency (a high directional antennais required). A narrow beam width generated by the high directionalantenna reduces interference. A small wavelength of the THz signal mayallow even more antenna elements to be integrated into a device and a BSwhich operate in this band. Through this, an advanced adaptivearrangement technology capable of range limitation may be used.

FIG. 11 illustrates an example of THz communication application.

As illustrated in FIG. 11 , a THz wireless communication scenario may becategorized into a macro network, a micro network, and a nanoscalenetwork. In the macro network, the THz wireless communication may beapplied to a vehicle-to-vehicle connection and a backhaul/fronthaulconnection. In the micro network, the THz wireless communication may beapplied to an indoor small cell, a fixed point-to-point or multi-pointconnection such as wireless connection in a data center, and near-fieldcommunication such as kiosk downloading.

Table 2 below is a table that shows an example a technology which may beused in the THz wave.

TABLE 2 Transceivers Device Available immature: UTC-PD, RTD and SBDModulation and coding Low order modulation techniques (OOK, QPSK), LDPC,Reed Soloman, Hamming, Polar, Turbo Antenna Omni and Directional, phasedarray with low number of antenna elements Bandwidth 69 GHz (or 23 GHz)at 300 GHz Channel models Partially Data rate 100 Gbps Outdoordeployment No Free space loss High Coverage Low Radio Measurements 300GHz indoor Device size Few micrometers

Optical Wireless Technology

An OWC technology is planed for 6G communication in addition to RF basedcommunication for all available device-to-access networks. The networkis connected to a network-to-backhaul/fronthaul network connection. TheOWC technology is already used after the 4G communication system, butmay be more widely used for meeting the requirements of the 6Gcommunication system. The OWC technologies such as light fidelity,visible light communication, optical commerce communication, and FSOcommunication based on the wideband are already well known technologies.The optical wireless technology based communication may provide a veryhigh data speed, a low latency time, and safe communication. LiDAR mayalso be used for superultra high resolution 3D mapping in the 6Gcommunication based on the wideband.

FSO Backhaul Network

Transmitter and receiver features of the FSO system are similar to thefeatures of the optical fiber network. Therefore, data transmission ofthe FSO system is similar to that of the optical fiber system.Therefore, the FSO may become an excellent technology that provides thebackhaul connection in the 6G system jointly with the optical fibernetwork. When the FSO is used, very long-distance communication isenabled even in a distance of 10000 km or more. The FSO supports amassive backhaul connection for remote and non-remote regions such asthe seat, the space, the underwater, and an isolated island. The FSOalso supports a cellular BS connection.

Massive MIMO Technology

One of the core technologies for enhancing the spectrum efficiency isapplication of the MIMO technology. When the MIMO technology isenhanced, the spectrum efficiency is also enhanced. Therefore, themassive MIMO technology will be important in the 6G system. Since theMIMO technology uses multiple paths, a multiplexing technology and abeam generation and operating technology suitable for the THz bandshould also be considered importantly so that the data signal istransmitted through one or more paths.

Blockchain

The blockchain will become an important technology for managing massdata in a future communication system. The blockchain is a form of adistributed ledger technology, and the distributed ledger is a databasedistributed in numerous nodes or computing devices. Each node replicatesand stores the same ledger copy. The blockchain is managed by a P2Pnetwork. The blockchain may be present without being managed by acentralized agency or server, but may be present. Data of the blockchainis jointly collected and constituted by blocks. The blocks are connectedto each other, and protected by using encryption. The blockchainfundamentally perfectly complements massive IoT through enhancedinteroperability security, personal information protection, stability,and scalability. Therefore, the blockchain technology provides variousfunctions such as inter-device interoperability, massive datatraceability, autonomous interactions of different IoT systems, andmassive connection stability of the 6G communication system.

3D Networking

The 6G system supports user communication of vertical scalability byintegrating ground and aerial networks. A 3D BS will be provided througha low-orbit satellite and a UAV. When a new dimension is added in termsof an altitude and a related degree of freedom, a 3D connection is quiredifferent from the existing 2D network.

Quantum Communication

In the context of the 6G network, unsupervised reinforcement learning ofthe network is promising. The supervised learning scheme may designate alabel in a vast amount of data generated in the 6G. The labeling is notrequired in the unsupervised learning. Therefore, the technology may beused for autonomously constructing a complicated network expression.When the reinforcement learning and the unsupervised learning arecombined, the network may be operated by a true autonomous scheme.

Unmanned Aerial Vehicle (UAV)

The unmanned aerial vehicle (UAV) or a drone will become an importantelement in the 6G wireless communication. In most cases, a high-speeddata wireless connection is provided by using a UAV technology. A BSentity is installed in the UAV in order to provide the cellularconnection. The UAV has a specific function which may not be seen in afixed BS infrastructure, such as easy deployment, strong visible-linelink, the degree of freedom in which mobility is controlled. Duringemergency situations such as natural disaster, the placement of a groundcommunication infrastructure is not enabled to be economically realized,and sometimes may not provide services in volatile environments. The UAVmay easily handle this situation. The UAV will become a new paradigm ofa wireless communication field. This technology facilitates three basicrequirements of the wireless network, i.e., eMBB, URLLC, and mMTC/TheUAV may also support various purposes such as network connectivityenhancement, fire sensing, disaster emergency services, securing andmonitoring, pollution monitoring, parking monitoring, accidentmonitoring, etc. Therefore, the UAV technology is recognized as one ofthe most important technologies for the 6G communication.

Cell-Free Communication

The close integration of multiple frequencies and heterogeneouscommunication technologies is very important in the 6G system. As aresult, the user may move smoothly from the network to another networkwithout the need of creating any manual configuration in the device. Abest network is automatically selected in an available communicationtechnology. This will break the limitation of a cell concept in thewireless communication. Currently, user movement from one cell toanother cell causes too many handovers in the network, and causeshandover failures, handover latency, data loss, and pingpong effects. 6Gcell-free communication will overcome all of the problems, and provide abetter QoS. The cell-free communication will be achieved throughdifferent heterogeneous radios of multi-connectivity and multi-tierhybrid technologies and devices.

Wireless Information and Energy Transmission Integration

WIET uses the same field and wave as the wireless communication system.In particular, the sensor and the smartphone will be charged by usingwireless power transmission during communication. The WIET is apromising technology for extending the life-span of a battery chargingwireless system. Therefore, a device without the battery will besupported in the 6G communication.

Integration of Sensing and Communication

An autonomous wireless network is a function to continuously anenvironmental state which is dynamically changed, and exchangeinformation between different nodes. In the 6G, the sensing will beclosely integrated with the communication in order to an autonomoussystem.

Integration of Access Backhaul Network

In the 6G, the density of the access network will be enormous. Eachaccess network is connected by the backhaul network such as the opticalfiber and the FSO network. In order to cope with a very larger number ofaccess networks, there will be a close integration between the accessand the backhaul network.

Hologram and Beamforming

The beamforming is a signal processing procedure of adjusting an antennaarray in order to transmit a radio signal. The beamforming is a sub-setof a smart antenna or advanced antenna system. The beamformingtechnology has several advantages such as a high call-to-noise ratio,interference prevention and denial, and high network efficiency. Thehologram and beamforming (HBF) is a new beamforming method which issignificantly different from the MIMO system because a software-definedantenna is used. The HBF will be a very effective approach scheme forefficient and flexible transmission and reception of the signal in amulti-antenna communication device.

Big Data Analysis

The big data analysis is a complicated process for analyzing variouslarge-scale data sets or big data. This process guarantees perfect datamanagement by finding hidden data, and information such as a correlationand a customer tendency which may not be known. The big data iscollected from various sources such as a video, a social network, animage, and the sensor. This technology is widely used to processing vastdata in the 6G system.

Large Intelligent Surface (LIS)

A THz band signal has a strong straightness, so there may be a lot ofshade regions due to obstacles, and an LIS technology will be importantin which the LIS is installed near such a shade region to expand acommunication zone and to enable communication stability strengtheningand additional services. The LIS is an artificial surface made ofelectromagnetic materials, and may change propagations of incoming radiowaves and outgoing radio waves. The LIS may be shown as an extension ofmassive MIMO, but is different from the massive MIMO in terms of anarray structure and an operating mechanism. Further, the LIS has anadvantage of maintaining low power consumption in that the LIS operatesas a reconfigurable reflector having passive element, i.e., reflects thesignal only passively without using an active RF chain. Further, sinceeach passive reflector of the LIS should independently control a phaseshift of an incident signal, the reflector may be advantage for thewireless communication channel. By appropriately controlling the phaseshift through an LIS controller, a reflected signal may be gathered in atarget receiver in order to boost a received signal power.

The 6G communication technology described above may be applied incombination with methods proposed in the present disclosure to bedescribed below or may be supplemented to specify or clarify technicalfeatures of the methods proposed in the present disclosure. On the otherhand, the communication service proposed by the present disclosure maybe applied in combination with a communication service by 3G, 4G, and/or5G communication technology in addition to the 6G communicationtechnology described above.

Hereinafter, a digital conversion device of a wireless signal isdescribed in detail, which receives a wireless signal from an antennaand converts the received wireless signal to a digital signal.

FIG. 12 is a diagram illustrating a digital conversion device of awireless signal according to an embodiment of the present disclosure.

Referring to FIG. 12 , a digital conversion device of a wireless signalaccording to a first embodiment of the present disclosure includes firstto third antennas AT1, AT2, and AT3, a plurality of unit ADC portions,and encoders 1221, 1222, 1223, and 1230. Hereinafter, the embodimentbased on a flash ADC as the unit ADC portions is mainly described.

The antennas AT receive an RF signal. A phase of the RF signal receivedby the antennas AT is aligned with respect to an input signal of theunit ADCs 1211, 1212, and 1213. The phase of the signal received by theantennas AT are different with each other depending on an incidentangle, and the like. The phase of the received signal is aligned beforebeing inputted to a spatial ADC portion 1200, that is, in the state ofan analogue signal.

A plurality of unit ADC portions is one-to-one matched to the antennasAT1, AT2, and AT3, and each of the plurality of unit ADC portionsconverts the reception signal received by each of the antennas AT1, AT2,and AT3 to a digital signal.

A particular embodiment for the flash ADC based unit ADCs is as follows.

The unit ADC portions include an RF signal processor and a unit ADC. Afirst unit ADC portion includes a first RF signal processor 1110 and afirst unit ADC 1211, a second first unit ADC portion includes a secondRF signal processor 1210 and a second unit ADC 1212, and a third unitADC portion includes a third RF signal processor 1130 and a third unitADC 1213.

Each of the first to third RF signal processors 1110, 1120, and 1130receives a wireless signal and adjusts a voltage level of the wirelesssignal based on an automatic gain values (LNA offset, VGA offset). Inthe first embodiment, the RF signal processors 1110, 1120, and 1130include the first to third RF signal processors 1110, 1120, and 1130,but the number of the RF signal processors 1110, 1120, and 1130 is notlimited thereto.

Each of the RF signal processors 1110, 1120, and 1130 includes anantenna AT, a low noise amplifier (LNA), a variable gain adjuster (VGA),and a mixer.

The low noise amplifiers (LNAs) amplify the reception signal having verylow power level due to the influence of attenuation and noise in thesignal received by the antenna AT. The variable gain adjusters (VGAs)adjust an output voltage level of the low noise amplifier. The variablegain adjusters (VGAs) perform a gain adjustment for a down-convertedsignal by the mixer with respect to an input signal of the spatial ADCportion 1200.

The spatial ADC portion 1200 includes the unit ADCs 1211, 1212, and 1213and the encoders 1221, 1222, 1223, and 1230.

The spatial ADC portion 1200 performs the same function of a single ADCby using the plurality of unit ADCs 1211, 1212, and 1213. A dynamicrange of the spatial ADC portion 1200 is distributed by the plurality ofunit ADCs 1211, 1212, and 1213. For example, in the case that thevoltage level received by the antennas AT1, AT2, and AT3 is Vmin toVmax(in this case, Vmin<Vmax), the first unit ADC 1211 may convert thevoltage in Vmin to V1 range (in this case, V1 is a voltage greater thanVmin and smaller than Vmax) to a digital voltage, the second unit ADC1212 may convert the voltage in V1 to V2 range (in this case, V2 is avoltage greater than V1 and smaller than Vmax) to a digital voltage, andthe third unit ADC 1213 may convert the voltage in V2 to Vmax range (inthis case, V3 is a voltage greater than V2 and smaller than Vmax) to adigital voltage.

In addition, each of the unit ADCs 1211, 1212, and 1213 maydigital-convert the partial voltage level on a different timing.

FIG. 12 shows the spatial ADC portion 1200 including the three unit ADCs1211, 1212, and 1213, but the number of unit ADCs is not limitedthereto. Each of the ADCs 1211, 1212, and 1213 receives an input of anin-phase signal (hereinafter, I signal) and a quadrature-phase signal(hereinafter, Q signal) of each of the RF signal processors and each ofthe plurality of unit ADCs 1211, 1212, and 1213 performs an analogue todigital conversion based on a different differential reference voltage.

Each of the first to third unit ADCs 1211, 1212, and 1213 includes anin-phase unit ADC (I_ADC) and an antiphase unit ADC (Q_ADC). Each of thein-phase unit ADCs (I_ADCs) receives the I signal from the RF signalprocessor and converts the I signal to a digital signal. The antiphaseunit ADC (Q_ADC) receives the Q signal from the RF signal processor andconverts the Q signal to a digital signal.

Each of the first to third unit ADCs 1211, 1212, and 1213 performs ananalogue to digital conversion based on a different differentialreference voltage.

The encoders 1221,1222, 1223, and 1230 generate binary data of a bitlower than the binary data based on the binary data output from the unitADCs 1211, 1212, and 1213.

The encoders 1221,1222, 1223, and 1230 include the unit encoders1221,1222, and 1223 and the spatial encoder 1230. The unit encoders1221,1222, and 1223 generate a thermometer code from respective binarydata output from the unit ADCs 1211, 1212, and 1213. That is, the unitencoders 1221,1222, and 1223 are connected to the RF signal processors1110, 1120, and 1130 in one-to-one manner, and each of the unit encoders1221,1222, and 1223 converts the signal output from the respective RFsignal processors 1110, 1120, and 1130 to a digital signal.

The spatial encoder 1230 generates binary data of a smaller bit based onthe binary data output from each of the unit encoders 1221,1222, and1223. For example, m (m is a natural number) unit encoders 1221,1222,and 1223 may output m binary data, and the spatial encoder 1230 maygenerate binary data of n-bit (n is a natural number smaller than m).

The spatial encoder 1230 may output the generated binary data to abaseband 1300.

An example of the spatial encoder 1230 generating the binary data isdescribed as below.

The first unit ADC 1211 compares an output voltage of the first unit ADC1110 with a first differential reference voltage SVref1. When the outputvoltage of the first unit ADC 1110 is the first differential referencevoltage SVref1 or greater, the first unit ADC 1211 outputs binary datahaving a value of “1 ”. Alternatively, when the output voltage of thefirst unit ADC 1110 is less than the first differential referencevoltage SVref1, the first unit ADC 1211 outputs binary data having avalue of “0”.

The second unit ADC 1212 compares an output voltage of the second unitADC 1120 with a second differential reference voltage SVref2. When theoutput voltage of the second unit ADC 1120 is the second differentialreference voltage SVref2 or greater, the second unit ADC 1212 outputsbinary data having a value of “1”. Alternatively, when the outputvoltage of the second unit ADC 1120 is less than the second differentialreference voltage SVref2, the second unit ADC 1212 outputs binary datahaving a value of “0”.

The third unit ADC 1213 compares an output voltage of the third unit ADC1130 with a third differential reference voltage SVref3. When the outputvoltage of the third unit ADC 1130 is the third differential referencevoltage SVref3 or greater, the third unit ADC 1213 outputs binary datahaving a value of “1”. Alternatively, when the output voltage of thethird unit ADC 1130 is less than the third differential referencevoltage SVref3, the third unit ADC 1211 outputs binary data having avalue of “0”.

The first to third differential reference voltage SVref1 to SVref3 aregenerated in proportional to the reference voltage Vref, and the sizesthereof are differently configured.

When the spatial ADC portion 1200 outputs binary data of b-bit, thenumber (m) of the unit ADCs 1211, 1212, and 1213 may be set to “(2n−1)”.For example, as shown in FIG. 12 , when the spatial ADC portion 1200outputs binary data of 2-bit, the unit ADCs may be configured with thefirst to third unit ADCs 1211, 1212, and 1213.

In addition, m differential reference voltages applied to m unit ADCsmay be configured to “{i/(m+1)}×Vref” (here, i is a natural number of 1or more and m or less). For example, the first differential referencevoltage SVref1 applied to the first unit ADC 1211 may be configured to“{1/(3+1)}×Vref”, the second differential reference voltage SVref2applied to the second unit ADC 1212 may be configured to“{2/(3+1)}×Vref”, and the third differential reference voltage SVref3applied to the third unit ADC 1213 may be configured to“{3/(3+1)}×Vref”.

Each of the first to third differential reference voltage SVref1 toSVref3 may be generated inside of the first to third unit ADCs 1211,1212, and 1213, respectively, and provided from an exterior.

The reference voltage Vref is configured to implement an effectivedegree of precision of the unit encoders 1221,1222, and 1223 which aredistributed to the respective RF signal processors. The referencevoltage Vref may be preconfigured according to the resolution of thespatial ADC.

The spatial encoder 1230 generates binary data to be proportional to thenumber of binary data having a value of “1” output by the unit encoders1221,1222, and 1223. In the case that the number of unit encoders is 3,the binary data having a value of “1” may be “0 to 3”. In the case thatthe thermometer code of the spatial encoder 1230 is binary data of 3-bitconfigured with “A-B-C”, an output of the first unit encoder 1221determines the first bit corresponding to “A”, an output of the secondunit encoder 1222 determines the second bit corresponding to “B”, and anoutput of the third unit encoder 1223 determines the third bitcorresponding to “C”.

The spatial encoder 1230 may output a digital code of “00” based on thata bit of the thermometer code is “000”. In addition, the spatial encoder1230 may output a digital code of “01” based on that a bit of thethermometer code is “001”, output a digital code of “10” based on that abit of the thermometer code is “011”, and output a digital code of “11”based on that a bit of the thermometer code is “111”.

An automatic gain controller 1400 generates the automatic gain valuebased on the output of the spatial ADC.

FIGS. 13 and 14 are diagrams illustrating an operation of the automaticgain controller.

FIG. 13 is a diagram illustrating an operation of the automatic gaincontroller according to the first embodiment.

Referring to FIG. 13 , the automatic gain controller 1400 according tothe first embodiment generates the first and second automatic gainvalues (LNA offset, VGA offset) based on the output of the spatial ADC.

The automatic gain controller 1400 may obtain an instantaneous powerbased on the output of the spatial ADC portion 1200. That is, theautomatic gain controller 1400 may add a square of the I signal and asquare of the Q signal output from the spatial ADC portion 1200 andcalculate the instantaneous power of the spatial ADC portion 1200.

The automatic gain controller 1400 may calculate a difference betweenthe instantaneous power value of the spatial ADC portion 1200 and atarget power value Pd of the spatial ADC portion 1200.

Furthermore, the automatic gain controller 1400 obtains hysteresis ofthe difference value between the instantaneous power value and thetarget power value Pd by using a loop filter. In addition, the automaticgain controller 1400 may search a gain control table and obtain thefirst automatic gain value LNA offset and the second automatic gainvalue VGA offset that correspond to the difference value between theinstantaneous power value and the target power value Pd. The automaticgain controller 1400 may use a calibration table in the process ofobtaining the first automatic gain value LNA offset and the secondautomatic gain value VGA offset.

FIG. 14 is a diagram illustrating an operation of the automatic gaincontroller according to the first embodiment.

Referring to FIG. 14 , the automatic gain controller 1400 according tothe second embodiment generates the first and second automatic gainvalues (LNA offset, VGA offset) based on the output of the spatial ADC.

The automatic gain controller 1400 may obtain an instantaneous powerbased on the output from each of the unit ADCs 1211, 1212, and 1213. Forexample, the automatic gain controller 1400 may add a square of the Isignal and a square of the Q signal output from the first unit ADC 1211and calculate the instantaneous power of the first unit ADC 1211.Likewise, the automatic gain controller 1400 may obtain an instantaneouspower of each of the second unit ADC 1212 and the third unit ADC 1213.

The automatic gain controller 1400 may calculate the instantaneous powerof the spatial ADC portion 1200 by adding the instantaneous power ofeach of the first to third unit ADCs 1211, 1212, and 1213.

The automatic gain controller 1400 calculates a difference between theinstantaneous power value of the spatial ADC portion 1200 and the targetpower value Pd.

Furthermore, the automatic gain controller 1400 obtains hysteresis ofthe difference value between the instantaneous power value of thespatial ADC portion 1200 and the target power value Pd of the spatialADC portion 1200 by using a loop filter. In addition, the automatic gaincontroller 1400 may search a gain control table and obtain the firstautomatic gain value LNA offset and the second automatic gain value VGAoffset that correspond to the difference value between the instantaneouspower value and the target power value Pd. The automatic gain controller1400 may use a calibration table in the process of obtaining the firstautomatic gain value LNA offset and the second automatic gain value VGAoffset.

The first automatic gain value LNA offset may be adjusted such that adifference of the output signal levels among the low noise amplifier(LNA) is minimized.

In addition, the automatic gain controller 1400 may activate ordeactivated selectively each of the RF signal processors 1110, 1120, and1130. The spatial ADC portion 1200 operates as a single virtual ADC byusing a plurality of the reception antennas AT and a plurality of the RFsignal processors 1110, 1120, and 1130. That is, each of the receptionantennas AT and the RF signal processors 1110, 1120, and 1130 has itsown operating area (dynamic range). In the case that the voltage levelof the signal received in an arbitrary reception antenna AT is apreconfigured threshold voltage or lower, the automatic gain controller1400 may control the first and second automatic gain values (LNA offsetand VGA offset) to deactivate the RF signal processor to which thereception antenna AT is belonged.

In the embodiment of the present disclosure, the automatic gaincontroller 1400 may be included in the baseband 1300 or implementedoutside of the baseband 1300.

Each of the unit spatial ADCs operates based on a common clock and onthe same timing. Accordingly, an occurrence of an input sampling signalmismatch may be prevented among the unit spatial ADCs. For this, asynchronizer or error correction circuit for synchronizing the commonclock may be included.

FIG. 15 is a diagram illustrating a digital conversion device of awireless signal according to another embodiment of the presentdisclosure.

As shown in FIG. 15 , a digital conversion device of a wireless signalaccording to another embodiment may include a first spatial ADC portion1200-1 and a second spatial ADC portion 1200-2.

The first and second spatial ADC portion 1200-1 and 1200-2 may generatethree binary data according to a voltage received from each of theplurality of RF signal processors 1110, 1120, and 1130, and based onthis, output binary data of 2-bit. The binary data of 2-bit output fromeach of the first and second spatial ADC portion 1200-1 and 1200-2 maybe provided to the single baseband 1300. The first and second spatialADC portion 1200-1 and 1200-2 may use the same reference voltage Vrefand based on this, generate the same differential reference voltageSVref1 to SVref3.

FIG. 16 is a diagram illustrating a digital conversion device of awireless signal according to still another embodiment of the presentdisclosure.

As shown in FIG. 16 , a digital conversion device of a wireless signalaccording to still another embodiment may include the first spatial ADCportion 1200-1, the second spatial ADC portion 1200-2, and one or morenormal ADC N_ADCs.

The first and second spatial ADC portion 1200-1 and 1200-2 may generatethree binary data according to a voltage received from each of theplurality of RF signal processors 1110, 1120, and 1130, and based onthis, output binary data of 2-bit. The binary data of 2-bit output fromeach of the first and second spatial ADC portion 1200-1 and 1200-2 maybe provided to the single baseband 1300. The first and second spatialADC portion 1200-1 and 1200-2 may use the same reference voltage Vrefand based on this, generate the same differential reference voltageSVref1 to SVref3.

Each of the normal ADC N_ADCs converts a signal received from a singlereception antenna to a digital signal.

The data output by the first and second spatial ADC portion 1200-1 and1200-2, and the normal ADC N_ADCs are provided to the single baseband1300.

Devices Used in Wireless Communication Systems

Although not limited thereto, various proposals of the presentdisclosure described above can be applied to various fields requiringwireless communication/connection (eg, 6G) between devices.

Hereinafter, it will be more specifically illustrated with reference tothe drawings. In the following drawings/description, the same referencenumerals may represent the same or corresponding hardware blocks,software blocks or functional blocks unless otherwise specified.

FIG. 17 illustrates a communication system 1 applied to the presentdisclosure.

Referring to FIG. 17 , a communication system 1 applied to the presentdisclosure includes wireless devices, Base Stations (BSs), and anetwork. Herein, the wireless devices represent devices performingcommunication using Radio Access Technology (RAT) (e.g., 5G New RAT(NR)) or Long-Term Evolution (LTE)) and may be referred to ascommunication/radio/5G devices. The wireless devices may include,without being limited to, a robot 100 a, vehicles 100 b-1 and 100 b-2,an eXtended Reality (XR) device 100 c, a hand-held device 100 d, a homeappliance 100 e, an Internet of Things (IoT) device 100 f, and anArtificial Intelligence (AI) device/server 400. For example, thevehicles may include a vehicle having a wireless communication function,an autonomous driving vehicle, and a vehicle capable of performingcommunication between vehicles. Herein, the vehicles may include anUnmanned Aerial Vehicle (UAV) (e.g., a drone). The XR device may includean Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR) deviceand may be implemented in the form of a Head-Mounted Device (HMD), aHead-Up Display (HUD) mounted in a vehicle, a television, a smartphone,a computer, a wearable device, a home appliance device, a digitalsignage, a vehicle, a robot, etc. The hand-held device may include asmartphone, a smartpad, a wearable device (e.g., a smartwatch or asmartglasses), and a computer (e.g., a notebook). The home appliance mayinclude a TV, a refrigerator, and a washing machine. The IoT device mayinclude a sensor and a smartmeter. For example, the BSs and the networkmay be implemented as wireless devices and a specific wireless device200 a may operate as a BS/network node with respect to other wirelessdevices.

The wireless devices 100 a to 100 f may be connected to the network 300via the BSs 200. An AI technology may be applied to the wireless devices100 a to 100 f and the wireless devices 100 a to 100 f may be connectedto the AI server 400 via the network 300. The network 300 may beconfigured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g.,NR) network. Although the wireless devices 100 a to 100 f maycommunicate with each other through the BSs 200/network 300, thewireless devices 100 a to 100 f may perform direct communication (e.g.,sidelink communication) with each other without passing through theBSs/network. For example, the vehicles 100 b-1 and 100 b-2 may performdirect communication (e.g. Vehicle-to-Vehicle(V2V)/Vehicle-to-everything (V2X) communication). The IoT device (e.g.,a sensor) may perform direct communication with other IoT devices (e.g.,sensors) or other wireless devices 100 a to 100 f.

Wireless communication/connection 150 a, 150 b may be performed betweenthe wireless devices 100 a to 100 f/base station 200-base station200/wireless devices 100 a to 100 f. Here, wirelesscommunication/connection may be performed through various radio accesstechnologies (eg, 5G NR) for uplink/downlink communication 150 a andsidelink communication 150 b (or D2D communication). Through thewireless communication/connection 150 a and 150 b, the wireless deviceand the base station/wireless device may transmit/receive radio signalsto each other. For example, the wireless communication/connection 150 aand 150 b may transmit/receive signals through various physical channelsbased on all/partial processes of FIG. To this end, based on the variousproposals of the present disclosure, various configuration informationsetting processes for transmitting/receiving radio signals, varioussignal processing processes (eg, channel encoding/decoding,modulation/demodulation, resource mapping/demapping, etc.) At least apart of a resource allocation process may be performed.

FIG. 18 illustrates wireless devices applicable to the presentdisclosure.

Referring to FIG. 18 , a first wireless device 100 and a second wirelessdevice 200 may transmit radio signals through a variety of RATs (e.g.,LTE and NR). Herein, {the first wireless device 100 and the secondwireless device 200} may correspond to {the wireless device 100 x andthe BS 200} and/or {the wireless device 100 x and the wireless device100 x} of FIG. 17 .

The first wireless device 100 may include one or more processors 102 andone or more memories 104 and additionally further include one or moretransceivers 106 and/or one or more antennas 108. The processor(s) 102may control the memory(s) 104 and/or the transceiver(s) 106 and may beconfigured to implement the descriptions, functions, procedures,proposals, methods, and/or operational flowcharts disclosed in thisdocument. For example, the processor(s) 102 may process informationwithin the memory(s) 104 to generate first information/signals and thentransmit radio signals including the first information/signals throughthe transceiver(s) 106. The processor(s) 102 may receive radio signalsincluding second information/signals through the transceiver 106 andthen store information obtained by processing the secondinformation/signals in the memory(s) 104. The memory(s) 104 may beconnected to the processor(s) 102 and may store a variety of informationrelated to operations of the processor(s) 102. For example, thememory(s) 104 may store software code including commands for performinga part or the entirety of processes controlled by the processor(s) 102or for performing the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document.Herein, the processor(s) 102 and the memory(s) 104 may be a part of acommunication modem/circuit/chip designed to implement RAT (e.g., LTE orNR). The transceiver(s) 106 may be connected to the processor(s) 102 andtransmit and/or receive radio signals through one or more antennas 108.Each of the transceiver(s) 106 may include a transmitter and/or areceiver. The transceiver(s) 106 may be interchangeably used with RadioFrequency (RF) unit(s). In the present disclosure, the wireless devicemay represent a communication modem/circuit/chip.

The second wireless device 200 may include one or more processors 202and one or more memories 204 and additionally further include one ormore transceivers 206 and/or one or more antennas 208. The processor(s)202 may control the memory(s) 204 and/or the transceiver(s) 206 and maybe configured to implement the descriptions, functions, procedures,proposals, methods, and/or operational flowcharts disclosed in thisdocument. For example, the processor(s) 202 may process informationwithin the memory(s) 204 to generate third information/signals and thentransmit radio signals including the third information/signals throughthe transceiver(s) 206. The processor(s) 202 may receive radio signalsincluding fourth information/signals through the transceiver(s) 106 andthen store information obtained by processing the fourthinformation/signals in the memory(s) 204. The memory(s) 204 may beconnected to the processor(s) 202 and may store a variety of informationrelated to operations of the processor(s) 202. For example, thememory(s) 204 may store software code including commands for performinga part or the entirety of processes controlled by the processor(s) 202or for performing the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document.Herein, the processor(s) 202 and the memory(s) 204 may be a part of acommunication modem/circuit/chip designed to implement RAT (e.g., LTE orNR). The transceiver(s) 206 may be connected to the processor(s) 202 andtransmit and/or receive radio signals through one or more antennas 208.Each of the transceiver(s) 206 may include a transmitter and/or areceiver. The transceiver(s) 206 may be interchangeably used with RFunit(s). In the present disclosure, the wireless device may represent acommunication modem/circuit/chip.

Hereinafter, hardware elements of the wireless devices 100 and 200 willbe described more specifically. One or more protocol layers may beimplemented by, without being limited to, one or more processors 102 and202. For example, the one or more processors 102 and 202 may implementone or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP,RRC, and SDAP). The one or more processors 102 and 202 may generate oneor more Protocol Data Units (PDUs) and/or one or more Service Data Unit(SDUs) according to the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document. Theone or more processors 102 and 202 may generate messages, controlinformation, data, or information according to the descriptions,functions, procedures, proposals, methods, and/or operational flowchartsdisclosed in this document. The one or more processors 102 and 202 maygenerate signals (e.g., baseband signals) including PDUs, SDUs,messages, control information, data, or information according to thedescriptions, functions, procedures, proposals, methods, and/oroperational flowcharts disclosed in this document and provide thegenerated signals to the one or more transceivers 106 and 206. The oneor more processors 102 and 202 may receive the signals (e.g., basebandsignals) from the one or more transceivers 106 and 206 and acquire thePDUs, SDUs, messages, control information, data, or informationaccording to the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document.

The one or more processors 102 and 202 may be referred to ascontrollers, microcontrollers, microprocessors, or microcomputers. Theone or more processors 102 and 202 may be implemented by hardware,firmware, software, or a combination thereof. As an example, one or moreApplication Specific Integrated Circuits (ASICs), one or more DigitalSignal Processors (DSPs), one or more Digital Signal Processing Devices(DSPDs), one or more Programmable Logic Devices (PLDs), or one or moreField Programmable Gate Arrays (FPGAs) may be included in the one ormore processors 102 and 202. The descriptions, functions, procedures,proposals, methods, and/or operational flowcharts disclosed in thisdocument may be implemented using firmware or software and the firmwareor software may be configured to include the modules, procedures, orfunctions. Firmware or software configured to perform the descriptions,functions, procedures, proposals, methods, and/or operational flowchartsdisclosed in this document may be included in the one or more processors102 and 202 or stored in the one or more memories 104 and 204 so as tobe driven by the one or more processors 102 and 202. The descriptions,functions, procedures, proposals, methods, and/or operational flowchartsdisclosed in this document may be implemented using firmware or softwarein the form of code, commands, and/or a set of commands.

The one or more memories 104 and 204 may be connected to the one or moreprocessors 102 and 202 and store various types of data, signals,messages, information, programs, code, instructions, and/or commands.The one or more memories 104 and 204 may be configured by Read-OnlyMemories (ROMs), Random Access Memories (RAMs), Electrically ErasableProgrammable Read-Only Memories (EPROMs), flash memories, hard drives,registers, cash memories, computer-readable storage media, and/orcombinations thereof. The one or more memories 104 and 204 may belocated at the interior and/or exterior of the one or more processors102 and 202. The one or more memories 104 and 204 may be connected tothe one or more processors 102 and 202 through various technologies suchas wired or wireless connection.

The one or more transceivers 106 and 206 may transmit user data, controlinformation, and/or radio signals/channels, mentioned in the methodsand/or operational flowcharts of this document, to one or more otherdevices. The one or more transceivers 106 and 206 may receive user data,control information, and/or radio signals/channels, mentioned in thedescriptions, functions, procedures, proposals, methods, and/oroperational flowcharts disclosed in this document, from one or moreother devices. For example, the one or more transceivers 106 and 206 maybe connected to the one or more processors 102 and 202 and transmit andreceive radio signals. For example, the one or more processors 102 and202 may perform control so that the one or more transceivers 106 and 206may transmit user data, control information, or radio signals to one ormore other devices. The one or more processors 102 and 202 may performcontrol so that the one or more transceivers 106 and 206 may receiveuser data, control information, or radio signals from one or more otherdevices. The one or more transceivers 106 and 206 may be connected tothe one or more antennas 108 and 208 and the one or more transceivers106 and 206 may be configured to transmit and receive user data, controlinformation, and/or radio signals/channels, mentioned in thedescriptions, functions, procedures, proposals, methods, and/oroperational flowcharts disclosed in this document, through the one ormore antennas 108 and 208. In this document, the one or more antennasmay be a plurality of physical antennas or a plurality of logicalantennas (e.g., antenna ports). The one or more transceivers 106 and 206may convert received radio signals/channels etc. from RF band signalsinto baseband signals in order to process received user data, controlinformation, radio signals/channels, etc. using the one or moreprocessors 102 and 202. The one or more transceivers 106 and 206 mayconvert the user data, control information, radio signals/channels, etc.processed using the one or more processors 102 and 202 from the baseband signals into the RF band signals. To this end, the one or moretransceivers 106 and 206 may include (analog) oscillators and/orfilters.

FIG. 19 illustrates a signal process circuit for a transmission signal.

Referring to FIG. 19 , a signal processing circuit 1000 may includescramblers 1010, modulators 1020, a layer mapper 1030, a precoder 1040,resource mappers 1050, and signal generators 1060. An operation/functionof FIG. 19 may be performed, without being limited to, the processors102 and 202 and/or the transceivers 106 and 206 of FIG. 18 . Hardwareelements of FIG. 19 may be implemented by the processors 102 and 202and/or the transceivers 106 and 206 of FIG. 18 . For example, blocks1010 to 1060 may be implemented by the processors 102 and 202 of FIG. 18. Alternatively, the blocks 1010 to 1050 may be implemented by theprocessors 102 and 202 of FIG. 18 and the block 1060 may be implementedby the transceivers 106 and 206 of FIG. 18 .

Codewords may be converted into radio signals via the signal processingcircuit 1000 of FIG. 19 . Herein, the codewords are encoded bitsequences of information blocks. The information blocks may includetransport blocks (e.g., a UL-SCH transport block, a DL-SCH transportblock). The radio signals may be transmitted through various physicalchannels (e.g., a PUSCH and a PDSCH).

Specifically, the codewords may be converted into scrambled bitsequences by the scramblers 1010. Scramble sequences used for scramblingmay be generated based on an initialization value, and theinitialization value may include ID information of a wireless device.The scrambled bit sequences may be modulated to modulation symbolsequences by the modulators 1020. A modulation scheme may includepi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying(m-PSK), and m-Quadrature Amplitude Modulation (m-QAM). Complexmodulation symbol sequences may be mapped to one or more transportlayers by the layer mapper 1030. Modulation symbols of each transportlayer may be mapped (precoded) to corresponding antenna port(s) by theprecoder 1040. Outputs z of the precoder 1040 may be obtained bymultiplying outputs y of the layer mapper 1030 by an N*M precodingmatrix W. Herein, N is the number of antenna ports and M is the numberof transport layers. The precoder 1040 may perform precoding afterperforming transform precoding (e.g., DFT) for complex modulationsymbols. Alternatively, the precoder 1040 may perform precoding withoutperforming transform precoding.

The resource mappers 1050 may map modulation symbols of each antennaport to time-frequency resources. The time-frequency resources mayinclude a plurality of symbols (e.g., a CP-OFDMA symbols and DFT-s-OFDMAsymbols) in the time domain and a plurality of subcarriers in thefrequency domain. The signal generators 1060 may generate radio signalsfrom the mapped modulation symbols and the generated radio signals maybe transmitted to other devices through each antenna. For this purpose,the signal generators 1060 may include Inverse Fast Fourier Transform(IFFT) modules, Cyclic Prefix (CP) inserters, Digital-to-AnalogConverters (DACs), and frequency up-converters.

Signal processing procedures for a signal received in the wirelessdevice may be configured in a reverse manner of the signal processingprocedures 1010 to 1060 of FIG. 19 . For example, the wireless devices(e.g., 100 and 200 of FIG. 18 ) may receive radio signals from theexterior through the antenna ports/transceivers. The received radiosignals may be converted into baseband signals through signal restorers.To this end, the signal restorers may include frequency downlinkconverters, Analog-to-Digital Converters (ADCs), CP remover, and FastFourier Transform (FFT) modules. Next, the baseband signals may berestored to codewords through a resource demapping procedure, apostcoding procedure, a demodulation processor, and a descramblingprocedure. The codewords may be restored to original information blocksthrough decoding. Therefore, a signal processing circuit (notillustrated) for a reception signal may include signal restorers,resource demappers, a postcoder, demodulators, descramblers, anddecoders.

FIG. 20 illustrates another example of a wireless device applied to thepresent disclosure. The wireless device may be implemented in variousforms according to a use-case/service (refer to FIG. 17 and FIGS. 21 toX9).

Referring to FIG. 20 , wireless devices 100 and 200 may correspond tothe wireless devices 100 and 200 of FIG. 18 and may be configured byvarious elements, components, units/portions, and/or modules. Forexample, each of the wireless devices 100 and 200 may include acommunication unit 110, a control unit 120, a memory unit 130, andadditional components 140. The communication unit may include acommunication circuit 112 and transceiver(s) 114. For example, thecommunication circuit 112 may include the one or more processors 102 and202 and/or the one or more memories 104 and 204 of FIG. 18 . Forexample, the transceiver(s) 114 may include the one or more transceivers106 and 206 and/or the one or more antennas 108 and 208 of FIG. 18 . Thecontrol unit 120 is electrically connected to the communication unit110, the memory 130, and the additional components 140 and controlsoverall operation of the wireless devices. For example, the control unit120 may control an electric/mechanical operation of the wireless devicebased on programs/code/commands/information stored in the memory unit130. The control unit 120 may transmit the information stored in thememory unit 130 to the exterior (e.g., other communication devices) viathe communication unit 110 through a wireless/wired interface or store,in the memory unit 130, information received through the wireless/wiredinterface from the exterior (e.g., other communication devices) via thecommunication unit 110.

The additional components 140 may be variously configured according totypes of wireless devices. For example, the additional components 140may include at least one of a power unit/battery, input/output (I/O)unit, a driving unit, and a computing unit. The wireless device may beimplemented in the form of, without being limited to, the robot (100 aof FIG. 17 ), the vehicles (100 b-1 and 100 b-2 of FIG. 17 ), the XRdevice (100 c of FIG. 17 ), the hand-held device (100 d of FIG. 17 ),the home appliance (100 e of FIG. 17 ), the IoT device (100 f of FIG. 17), a digital broadcast terminal, a hologram device, a public safetydevice, an MTC device, a medicine device, a fintech device (or a financedevice), a security device, a climate/environment device, the AIserver/device (400 of FIG. 17 ), the BSs (200 of FIG. 17 ), a networknode, etc. The wireless device may be used in a mobile or fixed placeaccording to a use-example/service.

In FIG. 20 , the entirety of the various elements, components,units/portions, and/or modules in the wireless devices 100 and 200 maybe connected to each other through a wired interface or at least a partthereof may be wirelessly connected through the communication unit 110.For example, in each of the wireless devices 100 and 200, the controlunit 120 and the communication unit 110 may be connected by wire and thecontrol unit 120 and first units (e.g., 130 and 140) may be wirelesslyconnected through the communication unit 110. Each element, component,unit/portion, and/or module within the wireless devices 100 and 200 mayfurther include one or more elements. For example, the control unit 120may be configured by a set of one or more processors. As an example, thecontrol unit 120 may be configured by a set of a communication controlprocessor, an application processor, an Electronic Control Unit (ECU), agraphical processing unit, and a memory control processor. As anotherexample, the memory 130 may be configured by a Random Access Memory(RAM), a Dynamic RAM (DRAM), a Read Only Memory (ROM)), a flash memory,a volatile memory, a non-volatile memory, and/or a combination thereof.

Hereinafter, an example of implementing FIG. 20 will be described indetail with reference to the drawings.

FIG. 21 illustrates a hand-held device applied to the presentdisclosure. The hand-held device may include a smartphone, a smartpad, awearable device (e.g., a smartwatch or a smartglasses), or a portablecomputer (e.g., a notebook). The hand-held device may be referred to asa mobile station (MS), a user terminal (UT), a Mobile Subscriber Station(MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or aWireless Terminal (WT).

Referring to FIG. 21 , a hand-held device 100 may include an antennaunit 108, a communication unit 110, a control unit 120, a memory unit130, a power supply unit 140 a, an interface unit 140 b, and an I/O unit140 c. The antenna unit 108 may be configured as a part of thecommunication unit 110. Blocks 110 to 130/140 a to 140 c correspond tothe blocks 110 to 130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive signals (e.g., dataand control signals) to and from other wireless devices or BSs. Thecontrol unit 120 may perform various operations by controllingconstituent elements of the hand-held device 100. The control unit 120may include an Application Processor (AP). The memory unit 130 may storedata/parameters/programs/code/commands needed to drive the hand-helddevice 100. The memory unit 130 may store input/output data/information.The power supply unit 140 a may supply power to the hand-held device 100and include a wired/wireless charging circuit, a battery, etc. Theinterface unit 140 b may support connection of the hand-held device 100to other external devices. The interface unit 140 b may include variousports (e.g., an audio 1/O port and a video 1/O port) for connection withexternal devices. The I/O unit 140 c may input or output videoinformation/signals, audio information/signals, data, and/or informationinput by a user. The I/O unit 140 c may include a camera, a microphone,a user input unit, a display unit 140 d, a speaker, and/or a hapticmodule.

As an example, in the case of data communication, the I/O unit 140 c mayacquire information/signals (e.g., touch, text, voice, images, or video)input by a user and the acquired information/signals may be stored inthe memory unit 130. The communication unit 110 may convert theinformation/signals stored in the memory into radio signals and transmitthe converted radio signals to other wireless devices directly or to aBS. The communication unit 110 may receive radio signals from otherwireless devices or the BS and then restore the received radio signalsinto original information/signals. The restored information/signals maybe stored in the memory unit 130 and may be output as various types(e.g., text, voice, images, video, or haptic) through the I/O unit 140c.

FIG. 22 illustrates a vehicle or an autonomous driving vehicle appliedto the present disclosure. The vehicle or autonomous driving vehicle maybe implemented by a mobile robot, a car, a train, a manned/unmannedAerial Vehicle (AV), a ship, etc.

Referring to FIG. 22 , a vehicle or autonomous driving vehicle 100 mayinclude an antenna unit 108, a communication unit 110, a control unit120, a driving unit 140 a, a power supply unit 140 b, a sensor unit 140c, and an autonomous driving unit 140 d. The antenna unit 108 may beconfigured as a part of the communication unit 110. The blocks110/130/140 a to 140 d correspond to the blocks 110/130/140 of FIG. 20 ,respectively.

The communication unit 110 may transmit and receive signals (e.g., dataand control signals) to and from external devices such as othervehicles, BSs (e.g., gNBs and road side units), and servers. The controlunit 120 may perform various operations by controlling elements of thevehicle or the autonomous driving vehicle 100. The control unit 120 mayinclude an Electronic Control Unit (ECU). The driving unit 140 a maycause the vehicle or the autonomous driving vehicle 100 to drive on aroad. The driving unit 140 a may include an engine, a motor, apowertrain, a wheel, a brake, a steering device, etc. The power supplyunit 140 b may supply power to the vehicle or the autonomous drivingvehicle 100 and include a wired/wireless charging circuit, a battery,etc. The sensor unit 140 c may acquire a vehicle state, ambientenvironment information, user information, etc. The sensor unit 140 cmay include an Inertial Measurement Unit (IMU) sensor, a collisionsensor, a wheel sensor, a speed sensor, a slope sensor, a weight sensor,a heading sensor, a position module, a vehicle forward/backward sensor,a battery sensor, a fuel sensor, a tire sensor, a steering sensor, atemperature sensor, a humidity sensor, an ultrasonic sensor, anillumination sensor, a pedal position sensor, etc. The autonomousdriving unit 140 d may implement technology for maintaining a lane onwhich a vehicle is driving, technology for automatically adjustingspeed, such as adaptive cruise control, technology for autonomouslydriving along a determined path, technology for driving by automaticallysetting a path if a destination is set, and the like.

For example, the communication unit 110 may receive map data, trafficinformation data, etc. from an external server. The autonomous drivingunit 140 d may generate an autonomous driving path and a driving planfrom the obtained data. The control unit 120 may control the drivingunit 140 a such that the vehicle or the autonomous driving vehicle 100may move along the autonomous driving path according to the driving plan(e.g., speed/direction control). In the middle of autonomous driving,the communication unit 110 may aperiodically/periodically acquire recenttraffic information data from the external server and acquiresurrounding traffic information data from neighboring vehicles. In themiddle of autonomous driving, the sensor unit 140 c may obtain a vehiclestate and/or surrounding environment information. The autonomous drivingunit 140 d may update the autonomous driving path and the driving planbased on the newly obtained data/information. The communication unit 110may transfer information about a vehicle position, the autonomousdriving path, and/or the driving plan to the external server. Theexternal server may predict traffic information data using AItechnology, etc., based on the information collected from vehicles orautonomous driving vehicles and provide the predicted trafficinformation data to the vehicles or the autonomous driving vehicles.

FIG. 23 illustrates a vehicle applied to the present disclosure. Thevehicle may be implemented as a transport means, an aerial vehicle, aship, etc.

Referring to FIG. 23 , a vehicle 100 may include a communication unit110, a control unit 120, a memory unit 130, an I/O unit 140 a, and apositioning unit 140 b. Herein, the blocks 110 to 130/140 a and 140 bcorrespond to blocks 110 to 130/140 of FIG. 20 .

The communication unit 110 may transmit and receive signals (e.g., dataand control signals) to and from external devices such as other vehiclesor BSs. The control unit 120 may perform various operations bycontrolling constituent elements of the vehicle 100. The memory unit 130may store data/parameters/programs/code/commands for supporting variousfunctions of the vehicle 100. The I/O unit 140 a may output an AR/VRobject based on information within the memory unit 130. The I/O unit 140a may include an HUD. The positioning unit 140 b may acquire informationabout the position of the vehicle 100. The position information mayinclude information about an absolute position of the vehicle 100,information about the position of the vehicle 100 within a travelinglane, acceleration information, and information about the position ofthe vehicle 100 from a neighboring vehicle. The positioning unit 140 bmay include a GPS and various sensors.

As an example, the communication unit 110 of the vehicle 100 may receivemap information and traffic information from an external server andstore the received information in the memory unit 130. The positioningunit 140 b may obtain the vehicle position information through the GPSand various sensors and store the obtained information in the memoryunit 130. The control unit 120 may generate a virtual object based onthe map information, traffic information, and vehicle positioninformation and the I/O unit 140 a may display the generated virtualobject in a window in the vehicle (1410 and 1420). The control unit 120may determine whether the vehicle 100 normally drives within a travelinglane, based on the vehicle position information. If the vehicle 100abnormally exits from the traveling lane, the control unit 120 maydisplay a warning on the window in the vehicle through the I/O unit 140a. In addition, the control unit 120 may broadcast a warning messageregarding driving abnormity to neighboring vehicles through thecommunication unit 110. According to situation, the control unit 120 maytransmit the vehicle position information and the information aboutdriving/vehicle abnormality to related organizations.

FIG. 24 illustrates an XR device applied to the present disclosure. TheXR device may be implemented by an HMD, an HUD mounted in a vehicle, atelevision, a smartphone, a computer, a wearable device, a homeappliance, a digital signage, a vehicle, a robot, etc.

Referring to FIG. 24 , an XR device 100 a may include a communicationunit 110, a control unit 120, a memory unit 130, an I/O unit 140 a, asensor unit 140 b, and a power supply unit 140 c. Herein, the blocks 110to 130/140 a to 140 c correspond to the blocks 110 to 130/140 of FIG. 20, respectively.

The communication unit 110 may transmit and receive signals (e.g., mediadata and control signals) to and from external devices such as otherwireless devices, hand-held devices, or media servers. The media datamay include video, images, and sound. The control unit 120 may performvarious operations by controlling constituent elements of the XR device100 a. For example, the control unit 120 may be configured to controland/or perform procedures such as video/image acquisition, (video/image)encoding, and metadata generation and processing. The memory unit 130may store data/parameters/programs/code/commands needed to drive the XRdevice 100 a/generate XR object. The I/O unit 140 a may obtain controlinformation and data from the exterior and output the generated XRobject. The I/O unit 140 a may include a camera, a microphone, a userinput unit, a display unit, a speaker, and/or a haptic module. Thesensor unit 140 b may obtain an XR device state, surrounding environmentinformation, user information, etc. The sensor unit 140 b may include aproximity sensor, an illumination sensor, an acceleration sensor, amagnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IRsensor, a fingerprint recognition sensor, an ultrasonic sensor, a lightsensor, a microphone and/or a radar. The power supply unit 140 c maysupply power to the XR device 100 a and include a wired/wirelesscharging circuit, a battery, etc.

For example, the memory unit 130 of the XR device 100 a may includeinformation (e.g., data) needed to generate the XR object (e.g., anAR/VR/MR object). The I/O unit 140 a may receive a command formanipulating the XR device 100 a from a user and the control unit 120may drive the XR device 100 a according to a driving command of a user.For example, when a user desires to watch a film or news through the XRdevice 100 a, the control unit 120 transmits content request informationto another device (e.g., a hand-held device 100 b) or a media serverthrough the communication unit 130. The communication unit 130 maydownload/stream content such as films or news from another device (e.g.,the hand-held device 100 b) or the media server to the memory unit 130.The control unit 120 may control and/or perform procedures such asvideo/image acquisition, (video/image) encoding, and metadatageneration/processing with respect to the content and generate/outputthe XR object based on information about a surrounding space or a realobject obtained through the I/O unit 140 a/sensor unit 140 b.

The XR device 100 a may be wirelessly connected to the hand-held device100 b through the communication unit 110 and the operation of the XRdevice 100 a may be controlled by the hand-held device 100 b. Forexample, the hand-held device 100 b may operate as a controller of theXR device 100 a. To this end, the XR device 100 a may obtain informationabout a 3D position of the hand-held device 100 b and generate andoutput an XR object corresponding to the hand-held device 100 b.

FIG. 25 illustrates a robot applied to the present disclosure. The robotmay be categorized into an industrial robot, a medical robot, ahousehold robot, a military robot, etc., according to a used purpose orfield.

Referring to FIG. 25 , a robot 100 may include a communication unit 110,a control unit 120, a memory unit 130, an I/O unit 140 a, a sensor unit140 b, and a driving unit 140 c. Herein, the blocks 110 to 130/140 a to140 c correspond to the blocks 110 to 130/140 of FIG. 20 , respectively.

The communication unit 110 may transmit and receive signals (e.g.,driving information and control signals) to and from external devicessuch as other wireless devices, other robots, or control servers. Thecontrol unit 120 may perform various operations by controllingconstituent elements of the robot 100. The memory unit 130 may storedata/parameters/programs/code/commands for supporting various functionsof the robot 100. The I/O unit 140 a may obtain information from theexterior of the robot 100 and output information to the exterior of therobot 100. The I/O unit 140 a may include a camera, a microphone, a userinput unit, a display unit, a speaker, and/or a haptic module. Thesensor unit 140 b may obtain internal information of the robot 100,surrounding environment information, user information, etc. The sensorunit 140 b may include a proximity sensor, an illumination sensor, anacceleration sensor, a magnetic sensor, a gyro sensor, an inertialsensor, an IR sensor, a fingerprint recognition sensor, an ultrasonicsensor, a light sensor, a microphone, a radar, etc. The driving unit 140c may perform various physical operations such as movement of robotjoints. In addition, the driving unit 140 c may cause the robot 100 totravel on the road or to fly. The driving unit 140 c may include anactuator, a motor, a wheel, a brake, a propeller, etc.

FIG. X9 illustrates an AI device applied to the present disclosure. TheAI device may be implemented by a fixed device or a mobile device, suchas a TV, a projector, a smartphone, a PC, a notebook, a digitalbroadcast terminal, a tablet PC, a wearable device, a Set Top Box (STB),a radio, a washing machine, a refrigerator, a digital signage, a robot,a vehicle, etc.

Referring to FIG. X9 , an AI device 100 may include a communication unit110, a control unit 120, a memory unit 130, an I/O unit 140 a/140 b, alearning processor unit 140 c, and a sensor unit 140 d. The blocks 110to 130/140 a to 140 d correspond to blocks 110 to 130/140 of FIG. 20 ,respectively.

The communication unit 110 may transmit and receive wired/radio signals(e.g., sensor information, user input, learning models, or controlsignals) to and from external devices such as other AI devices (e.g.,100 x, 200, or 400 of FIG. 17 ) or an AI server (e.g., 400 of FIG. 17 )using wired/wireless communication technology. To this end, thecommunication unit 110 may transmit information within the memory unit130 to an external device and transmit a signal received from theexternal device to the memory unit 130.

The control unit 120 may determine at least one feasible operation ofthe AI device 100, based on information which is determined or generatedusing a data analysis algorithm or a machine learning algorithm. Thecontrol unit 120 may perform an operation determined by controllingconstituent elements of the AI device 100. For example, the control unit120 may request, search, receive, or use data of the learning processorunit 140 c or the memory unit 130 and control the constituent elementsof the AI device 100 to perform a predicted operation or an operationdetermined to be preferred among at least one feasible operation. Thecontrol unit 120 may collect history information including the operationcontents of the AI device 100 and operation feedback by a user and storethe collected information in the memory unit 130 or the learningprocessor unit 140 c or transmit the collected information to anexternal device such as an AI server (400 of FIG. 17 ). The collectedhistory information may be used to update a learning model.

The memory unit 130 may store data for supporting various functions ofthe AI device 100. For example, the memory unit 130 may store dataobtained from the input unit 140 a, data obtained from the communicationunit 110, output data of the learning processor unit 140 c, and dataobtained from the sensor unit 140. The memory unit 130 may store controlinformation and/or software code needed to operate/drive the controlunit 120.

The input unit 140 a may acquire various types of data from the exteriorof the AI device 100. For example, the input unit 140 a may acquirelearning data for model learning, and input data to which the learningmodel is to be applied. The input unit 140 a may include a camera, amicrophone, and/or a user input unit. The output unit 140 b may generateoutput related to a visual, auditory, or tactile sense. The output unit140 b may include a display unit, a speaker, and/or a haptic module. Thesensing unit 140 may obtain at least one of internal information of theAI device 100, surrounding environment information of the AI device 100,and user information, using various sensors. The sensor unit 140 mayinclude a proximity sensor, an illumination sensor, an accelerationsensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGBsensor, an IR sensor, a fingerprint recognition sensor, an ultrasonicsensor, a light sensor, a microphone, and/or a radar.

The learning processor unit 140 c may learn a model consisting ofartificial neural networks, using learning data. The learning processorunit 140 c may perform AI processing together with the learningprocessor unit of the AI server (400 of FIG. 17 ). The learningprocessor unit 140 c may process information received from an externaldevice through the communication unit 110 and/or information stored inthe memory unit 130. In addition, an output value of the learningprocessor unit 140 c may be transmitted to the external device throughthe communication unit 110 and may be stored in the memory unit 130.

Here, the wireless communication technology implemented in the wirelessdevice (100, 200) of the present disclosure may include LTE, NR, and 6Gas well as Narrowband Internet of Things for low power communication. Atthis time, for example, NB-IoT technology may be an example of LPWAN(Low Power Wide Area Network) technology, and may be implemented instandards such as LTE Cat NB1 and/or LTE Cat NB2. not. Additionally oralternatively, the wireless communication technology implemented in thewireless device (100, 200) of the present disclosure may performcommunication based on LTE-M technology. At this time, as an example,LTE-M technology may be an example of LPWAN technology, and may becalled various names such as eMTC (enhanced machine type communication).For example, LTE-M technologies are 1) LTE CAT 0, 2) LTE Cat M1, 3) LTECat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTEMachine Type Communication, and/or 7) It may be implemented in at leastone of various standards such as LTE M, and is not limited to theabove-mentioned names. Additionally or alternatively, the wirelesscommunication technology implemented in the wireless device (100, 200)of the present disclosure is at least one of ZigBee, Bluetooth, and LowPower Wide Area Network (LPWAN) considering low power communication Itmay include any one, and is not limited to the above-mentioned names.For example, ZigBee technology can generate personal area networks(PANs) related to small/low-power digital communication based on variousstandards such as IEEE 802.15.4, and can be called various names.

Accordingly, the above detailed description should not be construed aslimiting in all respects and should be considered illustrative. Thescope of this disclosure should be determined by reasonableinterpretation of the appended claims, and all changes within theequivalent scope of this disclosure are included in the scope of thisdisclosure.

1. A digital conversion device of a wireless signal, comprising: aplurality of antennas for receiving a reception signal of apredetermined range of reception voltage level; a plurality of unit ADCportions one-to-one matched to the antennas and for converting a partialvoltage level within the range of the reception voltage level of thereception signal to binary data, wherein the plurality of unit ADCportions converts each of the partial voltage level having differentvoltage level to binary data; and an encoder for generating a digitalcode that a voltage level corresponding to the reception voltage levelis converted to binary data based on the binary data output from theunit ADC portions.
 2. The digital conversion device of a wireless signalof claim 1, wherein each of the unit ADC portions includes: a pluralityof RF signal processors for adjusting the voltage level of the receptionsignal based on an automatic gain value; and a plurality of unit ADCsfor receiving an input of an in-phase signal and a quadrature-phasesignal of each of the RF signal processors, wherein each of theplurality of unit ADCs performs an analogue to digital conversion basedon a differential reference voltage.
 3. The digital conversion device ofa wireless signal of claim 2, wherein the encoder generates binary dataof a bit lower than a bit of the binary data based on the binary dataoutput from the unit ADCs.
 4. The digital conversion device of awireless signal of claim 3, further comprising an automatic gaincontroller for generating the automatic gain value based on an output ofa spatial ADC.
 5. The digital conversion device of a wireless signal ofclaim 3, wherein the encoder includes: a plurality of unit encoders forgenerating a thermometer code from respective binary data output fromthe unit ADCs; and a spatial encoder for generating binary data of asmaller bit based on the binary data output from each of the unitencoders.
 6. The digital conversion device of a wireless signal of claim3, wherein each of the unit encoders outputs data of 1-bit.
 7. Thedigital conversion device of a wireless signal of claim 6, wherein theencoder includes m unit encoders, and wherein the encoder generatesbinary data of n-bit (n is a natural number smaller than m) based on mbinary data (m is a natural number) output from the unit encoders, andthe binary data of n-bit is generated to be proportional to a value of“1” among the m binary data.
 8. The digital conversion device of awireless signal of claim 7, wherein the number of unit encoders is setto “2^(n)−1”.
 9. The digital conversion device of a wireless signal ofclaim 7, wherein the differential reference voltage input to each of them spatial encoders is proportional by an integer multiple with eachother.
 10. The digital conversion device of a wireless signal of claim4, wherein each of the RF signal processors is provided with theautomatic gain value of a same size.
 11. The digital conversion deviceof a wireless signal of claim 10, wherein the automatic gain valueincludes: a first automatic gain value input to a low noise amplifierand a second automatic gain value input to a variable gain adjuster. 12.The digital conversion device of a wireless signal of claim 11, whereinthe first automatic gain value is adjusted such that a different ofoutput signal levels among the low noise amplifiers is less than apreconfigured threshold value.
 13. The digital conversion device of awireless signal of claim 11, wherein the automatic gain controllercontrols the first and second automatic gain values to deactivate the RFsignal processor to which the reception antenna is belonged, based onthe voltage level of a signal received through an arbitrary antennabeing a preconfigured threshold voltage or lower.
 14. The digitalconversion device of a wireless signal of claim 4, wherein the automaticgain controller generates the automatic gain value based on outputs ofthe unit encoders or the spatial encoders.
 15. The digital conversiondevice of a wireless signal of claim 14, wherein the automatic gaincontroller calculates an instantaneous power based on a square of thein-phase signal and a square of the quadrature-phase signal andcalculates the automatic gain value based on a hysteresis of thedifference value between the instantaneous power and a target powervalue.
 16. The digital conversion device of a wireless signal of claim1, wherein each of the spatial ADCs operates by using a common clock.17. The digital conversion device of a wireless signal of claim 1,wherein a phase of the reception signal is arranged with respect to aninput signal of the unit ADC.
 18. The digital conversion device of awireless signal of claim 1, wherein each of the spatial ADCsdigital-converts the partial voltage level on a different timing.